A hybrid VMD-LSTM/GRU model to predict non-stationary and irregular waves on the east coast of China

Ocean Engineering, Год журнала: 2023, Номер 276, С. 114136 - 114136

Опубликована: Март 28, 2023

Язык: Английский

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Air-quality prediction based on the ARIMA-CNN-LSTM combination model optimized by dung beetle optimizer

Scientific Reports, Год журнала: 2023, Номер 13(1)

Опубликована: Июль 26, 2023

Abstract Air pollution is a serious problem that affects economic development and people’s health, so an efficient accurate air quality prediction model would help to manage the problem. In this paper, we build combined accurately predict AQI based on real data from four cities. First, use ARIMA fit linear part of CNN-LSTM non-linear avoid blinding in hyperparameter setting. Then, dilemma setting, Dung Beetle Optimizer algorithm find hyperparameters model, determine optimal hyperparameters, check accuracy model. Finally, compare proposed with nine other widely used models. The experimental results show paper outperforms comparison models terms root mean square error (RMSE), absolute (MAE) coefficient determination (R 2 ). RMSE values for cities were 7.594, 14.94, 7.841 5.496; MAE 5.285, 10.839, 5.12 3.77; R 0.989, 0.962, 0.953 respectively.

Язык: Английский

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A novel machine learning-based artificial intelligence method for predicting the air pollution index PM2.5

Journal of Cleaner Production, Год журнала: 2024, Номер 468, С. 143042 - 143042

Опубликована: Июнь 28, 2024

Язык: Английский

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Apply a deep learning hybrid model optimized by an Improved Chimp Optimization Algorithm in PM2.5 prediction

Machine Learning with Applications, Год журнала: 2025, Номер unknown, С. 100624 - 100624

Опубликована: Янв. 1, 2025

Язык: Английский

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Modeling PM2.5 urbane pollution using hybrid models incorporating decomposition and multiple factors

Urban Climate, Год журнала: 2025, Номер 60, С. 102338 - 102338

Опубликована: Фев. 15, 2025

Язык: Английский

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Modeling PM2.5 forecast using a self-weighted ensemble GRU network: Method optimization and evaluation

Ecological Indicators, Год журнала: 2023, Номер 156, С. 111138 - 111138

Опубликована: Ноя. 6, 2023

Due to the rapid industrial development and global concern about air pollution, understanding dynamics of PM2.5 concentration has become a key aspect quality prediction. Many deep learning mode decomposition techniques have been explored capture temporal nonlinear features data. However, most existing methods ignore differences in prediction losses individual subsequences, resulting lower accuracy. To address this limitation, we proposed an ensemble gated recurrent unit (GRU) model that incorporated self-weighted total loss function based on variational (VMD). In approach, series were decomposed using VMD, then each subsequence (including residual sequence) was fed into GRU predicted calculated. For output optimal predictions, used adaptively optimize for subsequence. Specifically, larger weights assigned model's subsequences with higher predictive better focus those losses. addition, hyperparameter adjusted adapt various datasets different domains. Experimental results three show our performs than VMD-GRU single models. This validates effectiveness model. Our approach advantage plug-and-play, making it easier seamlessly integrate pattern

Язык: Английский

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An Integrated Complete Ensemble Empirical Mode Decomposition with Adaptive Noise to Optimize LSTM for Significant Wave Height Forecasting

Journal of Marine Science and Engineering, Год журнала: 2023, Номер 11(2), С. 435 - 435

Опубликована: Фев. 16, 2023

In recent years, wave energy has gained attention for its sustainability and cleanliness. As one of the most important parameters energy, significant height (SWH) is difficult to accurately predict due complex ocean conditions ubiquitous chaotic phenomena in nature. Therefore, this paper proposes an integrated CEEMDAN-LSTM joint model. Traditional computational fluid dynamics (CFD) a long calculation period high capital consumption, but artificial intelligence methods have advantage accuracy fast convergence. CEEMDAN commonly used method digital signal processing mechanical engineering, not yet been SWH prediction. It better performance than EMD EEMD more suitable LSTM addition, also novel filter formulation outliers based on improved violin-box plot. The final empirical results show that significantly outperforms each forecast duration, improving prediction accuracy. particular, duration 1 h, improvement over LSTM, with 71.91% RMSE, 68.46% MAE 6.80% NSE, respectively. summary, our model can improve real-time scheduling capability marine engineering maintenance operations.

Язык: Английский

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Predicting air quality index using attention hybrid deep learning and quantum-inspired particle swarm optimization

Journal Of Big Data, Год журнала: 2024, Номер 11(1)

Опубликована: Май 11, 2024

Abstract Air pollution poses a significant threat to the health of environment and human well-being. The air quality index (AQI) is an important measure that describes degree its impact on health. Therefore, accurate reliable prediction AQI critical but challenging due non-linearity stochastic nature particles. This research aims propose hybrid deep learning model based Attention Convolutional Neural Networks (ACNN), Autoregressive Integrated Moving Average (ARIMA), Quantum Particle Swarm Optimization (QPSO)-enhanced-Long Short-Term Memory (LSTM) XGBoost modelling techniques. Daily data were collected from official Seoul registry for period 2021 2022. first preprocessed through ARIMA capture fit linear part followed by architecture developed in pretraining–finetuning framework non-linear data. used convolution extract features original data, then QPSO optimize hyperparameter LSTM network mining long-terms time series features, was adopted fine-tune final model. robustness reliability resulting assessed compared with other widely models across meteorological stations. Our proposed achieves up 31.13% reduction MSE, 19.03% MAE 2% improvement R-squared best appropriate conventional model, indicating much stronger magnitude relationships between predicted actual values. overall results show attentive inspired more feasible efficient predicting at both city-wide station-specific levels.

Язык: Английский

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Prospective technical and technological insights into microalgae production using aquaculture wastewater effluents

Journal of Environmental Management, Год журнала: 2025, Номер 377, С. 124537 - 124537

Опубликована: Фев. 27, 2025

Microalgae biomass is a promising resource addressing climate change and play role in energy transition for generating biofuels. Due to their ability produce higher yield per year, biofuels obtained from microalgae are considered 3rd generation-advanced The industrial production of mitigates the effects CO2 emissions can be used wastewater bioremediation since most effluents rich nutrients. Using as growth media promotes principles circular economy nutrient recovery. aquaculture effluent contains high levels nitrogenous compounds, well phosphates dissolved organic carbon. current review aims identify, centralize, provide extensive information on decisive technological technical factors involved process different species wastewater. study focuses performance indicators, specific control strategies applied achieve pH control, it has been highlighted one important growth-related cofactors. A bibliometric framework was developed identify future trends integrated production. scientific literature analysis great potential production, due superior lipid carbohydrate productivity. Most systems found aim at controlling bioreactor by injecting CO2, while few other papers consider manipulating oxygen. need higher-level arises not only track or DO references but also maximize treatment efficiency bioreactor.

Язык: Английский

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PM2.5 probabilistic forecasting system based on graph generative network with graph U-nets architecture

Journal of Central South University, Год журнала: 2025, Номер 32(1), С. 304 - 318

Опубликована: Янв. 1, 2025

Язык: Английский

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